Contrastive learning is widely used in FGVC tasks. For example, TransFG[1]} learns better features through contrastive learning of features. API-Net[2]} uses ranking loss to learn the difference between a pair of image features. CCFR[3]} uses triplet loss to learn regions of discrimination. Contrastive learning seems to play an important role for the FGVC model. However, training a model through contrastive learning usually requires additional parameters, such as margin distance or temperature, etc. Because we hope to keep the training as simple as possible, contrastive learning is not used in this design, only Cross Entropy is used.
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